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Medical Image Analysis

  Our mission. To advance the state of the art in efficient machine learning and computer vision, and marry them with medical expertise to help with computer-aided diagnosis, personalized treatment and efficient data management. 

Project description. Analysis of medical images is essential in modern medicine. With the ever increasing amount of patient data, new challenges and opportunities arise for different phases of the clinical routine, such as diagnosis, treatment and monitoring.

The InnerEye research project focuses on the automatic analysis of patients' medical scans. It uses state of the art machine learning techniques for the:

  • automatic delineation and measurement of healthy anatomy and anomalies;
  • robust registration for monitoring disease progression;
  • semantic navigation and visualization for improved clinical workflow;
  • development of natural user interfaces for medical practitioners.

Some achievements. In Aug 2012 our algorithm for the automatic detection and localization of anatomy within Computed Tomography scans has obtained FDA 510(k) approval (ref. num. K120734).

- An overview of the InnerEye project


Demo videos

- Automatic 3D delineation of highly aggressive brain tumours

- Automatic localization and identification of vertebrae in 3D CT scans

- Kinect-based touchless interaction for surgery

- Automatic anatomy localization in 3D medical images

- Interactive segmentation of structures in 3D medical images

- Cloud-based volumetric rendering of medical images

Downloading free research data and tools

The new Medical Image Initiative is making available some of our annotated research data for everyone to use (for non-commercial purposes only).

More free tools and data may be downloaded from here.

Our scientific collaborations

Current collaborators include: Johns Hopkins Medical Institute, The Universty of Oxford, Cornell Medical School, Massachusetts General Hospital, University of Washington, Kings College London, INRIA Asclepios and Addenbrooke's NHS Hospital in Cambridge, amongst others.

Our work described by one of our radio-oncologist collaborators:

Our scientific publications